Alexander J Sundermann

Contributions to Public Health

Genomic epidemiology and machine learning for hospital outbreak detection and intervention. Since 2015, I have been part of the Microbial Genomic Epidemiology Laboratory (MiGEL) studying the impact of whole genome sequencing surveillance with machine learning of electronic health record data to more quickly detect and better intervene upon healthcare outbreaks compared to traditional infection prevention methods. Our results demonstrate that healthcare outbreaks are very often missed by traditional methods, significantly undercounted, and could save lives and costs attributed to these outbreaks.

1. Sundermann, A. J., Chen, J., Kumar, P., Ayres, A. M., Cho, S. T., Ezeonwuka, C., Griffith, M. P., Miller, J. K., Mustapha, M. M., Pasculle, A. W., Saul, M. I., Shutt, K. A., Srinivasa, V., Waggle, K., Snyder, D. J., Cooper, V. S., Van Tyne, D., Snyder, G. M., Marsh, J. W., Dubrawski, A., Harrison, L. H. (2021). Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection. Clinical infectious diseases, ciab946. Advance online publication. https://doi.org/10.1093/cid/ciab946
2. Sundermann, A. J., Chen, J., Miller, J., Martin, E., Snyder, G., Van Tyne, D., Marsh, J. W., Dubrawski, A., Harrison, L. (2022). Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary. Antimicrobial Stewardship & Healthcare Epidemiology, 2(1), E91. doi:10.1017/ash.2021.241
3. Sundermann, A. J., Babiker, A., Marsh, J. W., Shutt, K. A., Mustapha, M. M., Pasculle, A. W., Ezeonwuka, C., Saul, M. I., Pacey, M. P., Van Tyne, D., Ayres, A. M., Cooper, V. S., Snyder, G. M., & Harrison, L. H. (2020). Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance. Clinical infectious diseases, 70(11), 2336–2343. https://doi.org/10.1093/cid/ciz666

Advancing surveillance methods for healthcare-associated infections. I have collaborated with multi-institutional researchers to better define the current approaches to surveillance of healthcare-associated infections, understand the methodological limitations, and map a pathway for the future of surveillance. Through this collaborative work, we have identified key gaps in current surveillance techniques and have proposed novel methodologies that integrate real-time data analytics and advanced genomic tools.

1. Sundermann, A. J., Penzelik, J., Ayres, A., Snyder, G. M., & Harrison, L. H. (2023). Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance. Infection control and hospital epidemiology, 44(10), 1663–1665. https://doi.org/10.1017/ice.2023.52
2. Branch-Elliman, W., Sundermann, A. J., Wiens, J., & Shenoy, E. S. (2023). The future of automated infection detection: Innovation to transform practice (Part III/III). Antimicrobial stewardship & healthcare epidemiology: ASHE, 3(1), e26. https://doi.org/10.1017/ash.2022.333
3. Robillard, D. W., Sundermann A. J., Raux B. R., Prinzi A. M. (2024). Navigating the Network: A Narrative Overview of AMR Surveillance and Data Flow in the United States. Antimicrobial Stewardship & Healthcare Epidemiology. Accepted Manuscript

Evaluation and intervention on mold contaminated healthcare linen. In 2015, I helped lead an investigation into an outbreak of mucormycosis in transplant patients at UPMC. Our team traced the source back to healthcare linens that were contaminated at a third-party, off-site laundry facility. We addressed the issue of contamination and further evaluated the causal issue. We performed a national study of multiple hospitals that revealed high rates of mold contaminated clean linen arriving at hospitals. Taken together, our work in this area can help guide hospitals to identify and address mold contamination of healthcare laundry to increase patient safety.

1. Sundermann, A. J., Clancy, C. J., Pasculle, A. W., Liu, G., Cheng, S., Cumbie, R. B., Driscoll, E., Ayres, A., Donahue, L., Buck, M., Streifel, A., Muto, C. A., & Nguyen, M. H. (2021). Remediation of Mucorales-contaminated Healthcare Linens at a Laundry Facility Following an Investigation of a Case Cluster of Hospital-acquired Mucormycosis. Clinical infectious diseases, ciab638. Advance online publication. https://doi.org/10.1093/cid/ciab638
2. Sundermann, A. J., Clancy, C. J., Pasculle, A. W., Liu, G., Cumbie, R. B., Driscoll, E., Ayres, A., Donahue, L., Pergam, S. A., Abbo, L., Andes, D. R., Chandrasekar, P., Galdys, A. L., Hanson, K. E., Marr, K. A., Mayer, J., Mehta, S., Morris, M. I., Perfect, J., Revankar, S. G., Nguyen, M. H. (2019). How Clean Is the Linen at My Hospital? The Mucorales on Unclean Linen Discovery Study of Large United States Transplant and Cancer Centers. Clinical infectious diseases, 68(5), 850–853. https://doi.org/10.1093/cid/ciy669
3. Nguyen, M. H., Kaul, D., Muto, C., Cheng, S. J., Richter, R. A., Bruno, V. M., Liu, G., Beyhan, S., Sundermann, A. J., Mounaud, S., Pasculle, A. W., Nierman, W. C., Driscoll, E., Cumbie, R., Clancy, C. J., & Dupont, C. L. (2020). Genetic diversity of clinical and environmental Mucorales isolates obtained from an investigation of mucormycosis cases among solid organ transplant recipients. Microbial genomics, 6(12), mgen000473. https://doi.org/10.1099/mgen.0.000473